Baofeng Chang

Visual analysis has been increasingly integrated into the exploration of temporal networks, as visualization methods have the capability to present time-varying attributes and relationships of entities in an easy-to-read manner. Visualization techniques have been employed in a variety of dynamic network datasets, including social media networks, academic citation networks, and financial transaction networks. However, effectively visualizing dynamic basketball player network data, which consists of numerical networks, intensive timestamps, and subtle changes, remains a challenge for analysts. To address this issue, we propose a snapshot extraction algorithm that involves human-in-the-loop methodology to help users divide a series of networks into hierarchical snapshots for subsequent network analysis tasks, such as node exploration and network pattern analysis. Furthermore, we design and implement a prototype system, called DBNetVizor, for dynamic basketball player network data visualization. DBNetVizor integrates a graphical user interface to help users extract snapshots visually and interactively, as well as multiple linked visualization charts to display macro- and micro-level information of dynamic basketball player network data. To demonstrate the usability and efficiency of our proposed methods, we present two case studies based on dynamic basketball player network data in a competition. Additionally, we conduct an evaluation and receive positive feedback.

Video data refers to digital information in the form of a series of frames or images representing continuous motion captured by a video recording device. In various domains such as security, sports, education, and entertainment, a significant amount of video data is generated and stored daily. However, analyzing these videos manually is challenging due to their intrinsic characteristics, including large-scale, redundancy, contextual dependencies, and multimodality. Consequently, researchers have extensively explored visualization techniques to address these complexities. In this investigation, we review the state-of-the-art techniques in video visualization and visual analysis. Initially, we provide an overview of the design space for video visualization and visual analysis techniques. Subsequently, we organize and classify these techniques based on visual analysis tasks and application scenarios, providing detailed descriptions within each category. Drawing upon a comprehensive review of existing research, we provide a critical evaluation and propose potential opportunities for future research. Additionally, we have developed a web-based survey browser for convenient exploration of our created classification framework and the associated scholarly articles (https://zjutvis.github.io/VOVideo/).

Vision algorithms face challenges of limited visual presentation and unreliability in pedestrian activity assessment. In this article, we introduce LANDER, an interactive analysis system for visual exploration of pedestrian activity and uncertainty in surveillance videos. This visual analytics system focuses on three common categories of uncertainties in object tracking and action recognition. LANDER offers an overview visualization of activity and uncertainty, along with spatio-temporal exploration views closely associated with the scene. Expert evaluation and user study indicate that LANDER outperforms traditional video exploration in data presentation and analysis workflow. Specifically, compared to the baseline method, it excels in reducing retrieval time ( $p< $ 0.01), enhancing uncertainty identification ( $p< $ 0.05), and improving the user experience ( $p< $ 0.05).

Significant advancements in semantic parsing for text-to-SQL tasks have been achieved through the employment of neural network models, such as LSTM, BERT, and T5. The exceptional performance of large language models, like ChatGPT, has been demonstrated in recent research, even in zero-shot scenarios. However, the inherent transparency of text-to-SQL models presents them as black boxes, concealing their inner workings from both developers and users, which complicates the diagnosis of potential error patterns. Despite the fact that numerous visual analysis studies have been conducted in natural language processing communities, scant attention has been paid to addressing the challenges of semantic parsing, specifically in text-to-SQL tasks. This limitation hinders the development of effective tools for model optimization and evaluation. This paper presents an interactive visual analysis tool, MAVIDSQL, to assist model developers and users in understanding and diagnosing text-to-SQL tasks. The system comprises three modules: the Model Manager, the Feature Extractor, and the Visualization Interface, which adopt a model-agnostic approach to diagnose potential errors and infer model decisions by analyzing input-output data, facilitating interactive visual analysis to identify error patterns and assess model performance. Two case studies and interviews with domain experts demonstrate the effectiveness of MAVIDSQL in facilitating the understanding of text-to-SQL Tasks and identifying potential errors.

Carotid artery plaques can cause arterial vascular diseases such as stroke and myocardial infarction, posing a severe threat to human life. However, the current clinical examination mainly relies on a direct assessment by physicians of patients' clinical indicators and medical images, lacking an integrated visualization tool for analyzing the influencing factors and composition of carotid artery plaques. We have designed an intelligent carotid artery plaque visual analysis system for vascular surgery experts to comprehensively analyze the clinical physiological and imaging indicators of carotid artery diseases. The system mainly includes two functions: First, it displays the correlation between carotid artery plaque and various factors through a series of information visualization methods and integrates the analysis of patient physiological indicator data. Second, it enhances the interface guidance analysis of the inherent correlation between the components of carotid artery plaque through machine learning and displays the spatial distribution of the plaque on medical images. Additionally, we conducted two case studies on carotid artery plaques using real data obtained from a hospital, and the results indicate that our designed carotid analysis system can effectively provide clinical diagnosis and treatment guidance for vascular surgeons.